Many audio recordings are made in noisy environments. The presence of noise in audio recordings reduces their enjoyability and their intelligibility. Noise reduction algorithms are used to suppress background noise and improve the perceptual quality and intelligibility of audio recordings. Spectral attenuation is a common technique for removing noise from audio signals. Spectral attenuation involves applying a function of an estimate of the magnitude or power spectrum of the noise to the magnitude or power spectrum of the recorded audio signal. Another common noise reduction method involves minimizing the mean square error of the time domain reconstruction of an estimate of the audio recording for the case of zero-mean additive noise.
In general, these noise reduction methods tend to work well for audio signals that have high signal-to-noise ratios and low noise variability, but they tend to work poorly for audio signals that have low signal-to-noise ratios and high noise variability. What is needed is a noise reduction approach that yields good noise reduction results even when the audio signals have low signal-to-noise ratios and the noise content has high variability.